huggingface-community-evals

Run local Hugging Face model evaluations by using the inspect-ai or lighteval frameworks. This process executes benchmarks against models from the Hugging Face Hub directly on your local hardware, validating model performance without requiring remote services.

Benchmarking a local model using vLLM is supported through flexible backend selection. You can execute popular benchmarks like MMLU, GSM8K, and HellaSwag to validate fine-tuned models using vLLM for high-throughput GPU inference.

Yes, you can use Hugging Face Transformers instead of vLLM for local inference. The evaluation framework supports multiple inference backends, allowing you to easily switch to Transformers for compatibility or use accelerate for local hardware benchmarking.

Yes, local model evaluation supports smoke testing for performance validation. You can run a subset of benchmarks like MMLU to quickly validate a fine-tuned model on a local GPU before committing to full-scale remote evaluation.

Execute MMLU benchmarks using either lighteval or inspect-ai frameworks. The skill standardizes benchmarking by facilitating task selection and backend fallback strategies, allowing you to evaluate models like meta-llama/Llama-3.2-1B locally.